Reconstruction of nonuniformly sampled bandlimited signals

ABSTRACT

The present invention refers to a method and apparatus for reconstruction of a nonuniformly sampled bandlimited analog signal x a (t), said nonuniformly sampled signal comprising N subsequences x k (m), k=0, 1, . . . , N−1, N≧2, obtained through sampling at a sampling rate of 1/(MT) according to x k (m)=x a (nMT+t k ), where M is an integer, and t k =kMT/N+Δt k , Δt k  being different from zero. The invention comprises forming a new sequence y(n) from said N subsequences x k (m) such that y(n) at least contains the same information as x(n)=x a (nT), i.e. x a (t) sampled with a sampling rate of 1/T, in a frequency region lower than ω 0 , ω 0  being a predetermined limit frequency, by means of (i) upsampling each of said N subsequences x k (m), k=0, 1, . . . , N−1, by a factor M, M being a positive integer; (ii) filtering each of said upsampled N subsequences x k (m), k=0, 1, . . . , N−1, by a respective digital filter; and (iii) adding said N digitally filtered subsequences to form y(n). The respective digital filter is preferably a fractional delay filter and has preferably a frequency response G k =a k e (−jωsT) , k=0, 1, . . . , N−1, in the frequency band |ωT|≦ω 0 T, a k  being a constant and s=d+t k , d being an integer.

This application claims priority under 35 U.S.C. §§119 and/or 365 to 0003549-3 filed in Sweden on Oct. 2, 2000; the entire content of which is hereby incorporated by reference.

TECHNICAL FIELD OF THE INVENTION

The present invention generally relates to field of sampling, and more specifically, to methods and apparatus for reconstruction of nonuniformly sampled bandlimited signals, to methods and apparatus for compensation of time skew in time-interleaved analog-to-digital converters (ADCs), and to a computer program product for performing said methods of reconstruction.

DESCRIPTION OF RELATED ART AND BACKGROUND OF THE INVENTION

In uniform sampling, a sequence x(n) is obtained from an analog signal x_(a)(t) by sampling the latter equidistantly at t=nT, −∞<n<∞, i.e., x(n)=x_(a)(nT), T being the sampling period, as illustrated in FIG. 1a. In this case, the time between two consecutive sampling instances is always T. In nonuniform sampling, on the other hand, the time between two consecutive sample instances is dependent on the sampling instances. The present invention deals with the situation where the samples can be separated into N subsequences x_(k)(m), k=0, 1, . . . , N−1, where x_(k)(m) is obtained by sampling x_(a)(t) with the sampling rate 1/(MT) at t=nMT+t_(k), i.e., x_(k)(m)=x_(a)(nMT+t_(k)), M being a positive integer. This sampling scheme is illustrated in FIG. 1b for N=2 and M=2. Such nonuniformly sampled signals occur in, e.g., time-interleaved analog-to-digital converters (ADCs) due to time skew errors.

The question that arises is how to form a new sequence y(n) from x_(k)(m) such that y(n) is either exactly or approximately (in some sense) equal to x(n). For conventional time-interleaved ADCs, N=M and, ideally, t_(k)=kT. In this case, y(n)=x(n) is obtained by simply interleaving x_(k)(m). However, in practice, t_(k) is not exactly equal to kT due to time skew errors which introduces aliasing components into Y(e^(jωT)), Y(e^(jωT)) being the Fourier transform of y(n). This means that y(n)≠x(n), and thus the information in y(n) is no longer the same as that in x(n).

It should be noted that it is well known that, if the t_(k)'s are distinct such that all samples are separated in time, then x_(a)(t) is uniquely determined by the samples in the x_(k)(m)'s. It is also well known how to retain x_(a)(t) from the x_(k)(m)'s using analog interpolation functions. However, these functions are not easily, if at all possible, achievable in practical implementations, which thus call for other solutions.

SUMMARY OF THE INVENTION

Accordingly, it is an object of the present invention to provide a method and an apparatus, respectively, for reconstruction of a nonuniformly sampled bandlimited analog signal x_(a)(t), said nonuniformly sampled signal comprising N subsequences x_(k)(m), k=0, 1, . . . , N−1, N≧2, obtained through sampling at a sampling rate of 1/(MT) according to x_(k)(m)=x_(a)(nMT+t_(k)), where M is a positive integer, and t_(k)=kMT/N+Δt_(k), Δt_(k) being different from zero, which are capable of forming a new sequence y(n) from said N subsequences x_(k)(m) such that y(n) at least contains the same information as x(n)=x_(a)(nT), i.e. x_(a)(t) sampled with a sampling rate of 1/T, in a frequency region lower than ω₀ (and possibly including ω₀), ω₀ being a predetermined limit frequency.

A further object of the present invention is to provide such method and apparatus, respectively, which are efficient, fast, simple, and of low cost.

Still a further object of the present invention is to provide such method and apparatus, respectively, which are capable of reducing noise such as e.g. quantization noise.

Those objects among others are attained by a method and an apparatus, respectively, which perform the steps of:

(i) upsampling each of the N subsequences x_(k)(m), k=0, 1, . . . , N−1, by the factor M;

(ii) filtering each of the upsampled N subsequences x_(k)(m), k=0, 1, . . . , N−1, by a respective digital filter; and

(iii) adding the N digitally filtered subsequences to form y(n).

Preferably, the respective digital filter is a fractional delay filter and has a frequency response G_(k)=a_(k)e^((−jωsT)), k=0, 1, . . . , N−1, in the frequency band |ωT|≦ω₀T, a_(k) being a constant and s being different from an integer, and particularly s equals d+t_(k), d being an integer.

If ω₀T is a fixed value less than π, such that the original analog signal comprises frequency components of a higher frequency than ω₀, regional perfect reconstruction is achieved, i.e. y(n) contains the same information as x(n)=x_(a)(nT), i.e. x_(a)(t) sampled with a sampling rate of 1/T, only in a frequency region |ω|≦ω₀. Regionally perfect reconstruction is of particular interest in oversampled systems where the lower frequency components carry the essential information, whereas the higher frequency components contain undesired components (e.g., noise) to be removed by digital and/or analog filters.

Here, the fractional delay filters have a frequency response G_(k)=a_(k) A_(k)(e^(jωT)), k=0, 1, . . . , N−1, in the frequency band ω₀T<|ωT|≦π, where A_(k)(e^(jωT)) is an arbitrary complex function.

If on the other hand ω₀ does include all frequency components of the original analog signal (i.e. ω₀T includes all frequencies up to π) perfect reconstruction is achieved, i.e. y(n) is identical with x(n).

In either case two different situations arise: (1) 2K₀+1=N and (2) 2K₀+1<N, wherein K₀ is given by $K_{0} = {\left\lceil \frac{M\left( {{\omega_{0}T} + {\omega_{1}T}} \right)}{2\pi} \right\rceil - 1}$

for regionally perfect reconstruction, wherein ┌x┐ should be read as the smallest integer larger than or equal to x and [−ω₁, ω₁] being the frequency band wherein said bandlimited analog signal x_(a)(t) is found, respectively, and by

K ₀ =M−1

for perfect reconstruction.

In situation (1) the a_(k)'s are calculated as

a=B ⁻¹ c,

a being the a_(k)'s in vector form given by

a=[a ₀ a ₁ . . . a _(N−1)]^(T),

B⁻¹ being the inverse of B as given by ${B = \begin{bmatrix} u_{0}^{- K_{0}} & u_{1}^{- K_{0}} & \cdots & u_{N - 1}^{- K_{0}} \\ u_{0}^{- {({K_{0} - 1})}} & u_{1}^{- {({K_{0} - 1})}} & \cdots & u_{N - 1}^{- {({K_{0} - 1})}} \\ \vdots & \vdots & \quad & \vdots \\ u_{0}^{K_{0}} & u_{1}^{K_{0}} & \cdots & u_{N - 1}^{K_{0}} \end{bmatrix}},$

 wherein ${u_{k} = ^{{- j}\frac{2\pi}{MT}t_{k}}},$

and c being

 c=[c ₀ c ₁ . . . c _(2K) ₀ ]^(T),

 wherein $c_{k} = \left\{ {\begin{matrix} {M,} & {k = K_{0}} & \quad \\ {0,} & {{k = 0},1,\ldots \quad,{2K_{0}},} & {k \neq K_{0}} \end{matrix}.} \right.$

In situation (2) the a_(k)'s are calculated as

a={circumflex over (B)} ⁻¹ ĉ,

a being defined as

a=[a _(u) a _(fix)]^(T)

wherein a_(u) and a_(fix) contain (2K₀+1) unknown a_(k)'s and L=N−2K₀−1 fixed constant a_(k)'s, {circumflex over (B)}⁻¹ being the inverse of {circumflex over (B)} as given by ${\hat{B} = \begin{bmatrix} B \\ S \end{bmatrix}},$

wherein B is given by ${B = \begin{bmatrix} u_{0}^{- K_{0}} & u_{1}^{- K_{0}} & \cdots & u_{N - 1}^{- K_{0}} \\ u_{0}^{- {({K_{0} - 1})}} & u_{1}^{- {({K_{0} - 1})}} & \cdots & u_{N - 1}^{- {({K_{0} - 1})}} \\ \vdots & \vdots & \quad & \vdots \\ u_{0}^{K_{0}} & u_{1}^{K_{0}} & \cdots & u_{N - 1}^{K_{0}} \end{bmatrix}},$

 wherein ${u_{k} = ^{{- j}\frac{2\pi}{MT}t_{k}}},$

S is given by

S=[S _(z) S _(d)],

 wherein $S_{z} = \begin{bmatrix} 0 & 0 & \cdots & 0 \\ 0 & 0 & \cdots & 0 \\ \vdots & \vdots & \quad & \vdots \\ 0 & 0 & \cdots & 0 \end{bmatrix}$

 and

S _(d)=diag[1 1 . . . 1],

 and

ĉ being

ĉ=[c a _(fix)]^(T),

 wherein

c is given by

c=[c ₀ c ₁ . . . c _(2k) ₀ ]^(T),

 wherein $c_{k} = \left\{ {\begin{matrix} {M,} & {k = K_{0}} & \quad \\ {0,} & {{k = 0},1,\ldots \quad,{2K_{0}},} & {k \neq K_{0}} \end{matrix}.} \right.$

Thus L a_(k)'s can be arbitrarily chosen. Preferably they are chosen to be zero in which case the corresponding channel is removed or to be M/N in which case any quantization noise can be minimized.

Further objects of the invention are to provide a method for compensation of time skew in a time-interleaved analog-to-digital converter (ADC) system comprising a plurality of analog-to-digital converters (ADCs), and to provide the ADC system itself.

Thus, such method and ADC system are provided comprising the respective method and apparatus as described above, wherein each of the N subsequences x_(k)(m), k=0, 1, . . . , N−1, N≧2 is sampled by a respective one of the analog-to-digital converters.

Yet a further object of the present invention is to provide a computer program product for reconstruction of a nonuniformly sampled bandlimited analog signal.

Such object is attained by a computer program product loadable into the internal memory of a digital signal processing apparatus, comprising software code portions for performing any of the methods as depicted above when said product is run on said apparatus.

An advantage of the present invention is that a fully or partly reconstructed digital signal may be produced without the need of applying very complex and hardly implementable analog interpolation functions.

Further characteristics of the invention and advantages thereof will be evident from the following detailed description of embodiments of the invention.

BRIEF DESCRIPTION OF THE DRAWINGS

The present invention will become more fully understood from the detailed description of preferred embodiments of the present invention given hereinbelow and the accompanying FIGS. 1-6, which are given by way of illustration only, and thus are not limitative of the present invention.

FIG. 1a illustrates schematically uniform sampling, wherein a sequence x(n) is obtained from an analog signal x_(a)(t) by sampling the latter equidistantly at t=nT, −∞<n<∞, i.e., x(n)=x_(a)(nT); and FIG. 1b illustrates schematically nonuniform sampling, wherein samples are separated into two subsequences x_(k)(m), k=0, 1 where x_(k)(m) is obtained by sampling x_(a)(t) with the sampling rate 1/(2T) at t=n2T+t_(k), i.e., x_(k)(m)=x_(a)(n2T+t_(k)).

FIG. 2 illustrates schematically a uniform sampler and quantizer.

FIG. 3 illustrates schematically an upsampler.

FIG. 4 illustrates schematically a hybrid analog/digital filter bank ADC system.

FIG. 5 illustrates schematically an analysis filter bank system for producing x_(k)(m), k 0, 1, . . . , N−1, x_(k)(m) being N subsequences obtained through sampling of x_(a)(t) at the time instances t=nMT+t_(k).

FIG. 6 illustrates schematically a polyphase representation of the upsampling and synthesis bank in the system of FIG. 4.

DETAILED DESCRIPTION OF EMBODIMENTS

In the following description, for purposes of explanation and not limitation, specific details are set forth in order to provide a thorough understanding of the present invention. However, it will be apparent to one skilled in the art that the present invention may be practiced in other versions that depart from these specific details. In other instances, detailed descriptions of well-known methods and apparatuses are omitted so as not to obscure the description of the present invention with unnecessary details.

This invention considers the problem of reconstructing nonuniformly sampled bandlimited signals. Such problem arises in, e.g., time-interleaved analog-to-digital converters (ADCs) due to time skew errors. To be precise, we deal with the following situation. Given N subsequences x_(k)(m), k=0, 1, . . . , N−1, obtained through sampling of a bandlimited analog signal x_(a)(t) with a sampling rate of 1/(MT) according to x_(k)(m)=x_(a)(nMT+t_(k)). How to form a new sequence y(n) from x_(k)(m) such that y(n) is either exactly or approximately (in some sense) equal to x(n)=x_(a)(nT), i.e., x_(a)(t) sampled with a sampling rate of 1/T. To this end, we propose in this patent the use of an N-channel digital synthesis filter bank. The overall system can be viewed as a generalization of the conventional time-interleaved ADCs, to which the former reduces as a special case. We show that the proposed system, with proper ideal synthesis filters, can achieve y(n)=x(n). These synthesis filters are however not suitable to be approximated by practical digital filters. Therefore, we also consider the case in which y(n)≠x(n) but where y(n) and x(n) contain the same information in a lower frequency region. We show that the overall system can achieve Y(e^(jωT))=X(e^(jωT)) for |ωT|≦ω₀T, Y(e^(jωT)) and X(e^(jωT)) being the Fourier transforms of x(n) and y(n), respectively, and ω₀ being a predetermined limit frequency, again with proper ideal synthesis filters, which in this case can be approximated by practical digital filters. This scheme is useful for (slightly) oversampled ADC systems where aliasing into the frequency band ω₀T<|ωT|≦π can be tolerated. The ideal synthesis filters are allpass filters with, in general, different gain constants. We analyze the effects of using practical filters approximating the ideal ones.

The outline of the remaining parts of this description is as follows. Firstly, uniform sampling, upsampling, and hybrid analog/digital filter banks, the latter of which is convenient to use when analyzing nonuniformly sampled systems, are briefly recapitulated. The following section deals with nonuniform sampling and reconstruction. Thereafter, time-interleaved ADCs and their generalizations are considered. The subsequent section is concerned with error analysis and quantization noise, respectively. Finally, a list of equations (eqs.) is given, said equations being referred to in the above said sections.

Uniform Sampling, Upsampling, and Filter Banks

Uniform sampling and quantization are represented by the uniform sampler and quantizer in FIG. 2. Ignoring the quantization, the output sequence x(n) is obtained by sampling the analog input signal x_(a)(t) uniformly at the time instances nT, for all n, see eq. (1) in the list of equations at the end of this description. Here, T is the sampling period and f_(sample)=1/T is the sampling frequency. The Fourier transforms of x(n) and x_(a)(t) are related according to Poisson's summation formula, see eq. (2).

The upsampler in FIG. 3 is used to increase the sampling frequency by a factor of M. The sampling period and sampling frequency associated with the lower rate, denoted here by T₁ and f_(sample,1), respectively, are obviously related to T and f_(sample) as in eq. (3). The output sequence y(n) is given by eq. (4) and the Fourier transforms of y(n) and x(m) are related to each other as in eq. (5).

Consider the system in FIG. 4, which we refer to as a hybrid analog/digital filter bank or filter bank ADC. This system makes use of an analog analysis filter bank, uniform samplers and quantizers, and a digital synthesis filter bank. The sampling and quantization take place at the output of the analysis filters with a sampling frequency of 1/T=f_(sample)/M, since T₁=MT. In the filter bank ADC, both the sampling and quantizations are thus performed at the low sampling rate f_(sample)/M.

Ignoring the quantizations in the system of FIG. 4, the Fourier transform of the output sequence y(n) is easily obtained with the aid of the above relations, see eq. (6) wherein X_(k)(e^(jMωT)) is given by eq. (7). Equation (6) can be rewritten as eq. (8) where V_(p)(jω) is given by eq. (9).

Consider the systems in FIGS. 2 and 4 with X(e^(jωT)) and Y(e^(jωT)) as given by Eqs. (2) and (8), respectively. Recall that the spectrum of a sampled signal always is periodic with a period of 2π (2π-periodic). Thus, X(e^(jωT)) is apparently 2π-periodic. This holds true also for Y(e^(jωT)) as long as all G_(k)(e^(jωT)) are 2π-periodic. Thus, it suffices to consider X(e^(jωT)) and Y(e^(jωT)) in the interval −π≦ωT≦π. We will now treat two different types of reconstruction.

Perfect reconstruction: The system in FIG. 4 has perfect reconstruction (PR) if Eq. (10) prevails for some non-zero constant c and integer constant d. In the time-domain we have in the PR case y(n)=cx(n−d). That is, with c=1, y(n) is simply a shifted version of x(n). From Eqs. (2), (8), and (10), we see that PR is obtained if Eq. (11) prevails for −∞≦r≦∞.

Regionally perfect reconstruction: Let x(n) and y(n) be separated as given by eq. (12) with corresponding Fourier transforms given by eqs. (13) and (14) where ω₀T<π. The system in FIG. 4 has regionally perfect reconstruction (RPR) if eq. (15) or, equivalently, eq. (16) prevails for some non-zero constant c and integer constant d. In the time-domain we have in the RPR case y_(low)(n)=cx_(low)(n−d). That is, with c=1, y_(low)(n) is simply a shifted version of x_(low)(n). However, y(n) is not a shifted version of x(n), i.e., y(n)≠cx(n−d). From Eqs. (2), (8), and (16), we see that RPR is obtained if eq. (17) is fulfilled for −∞≦r≦∞. Regionally perfect reconstruction systems are of interest in oversampled systems where x_(low)(n) carries the essential information, whereas x_(high)(n) contains undesired components (e.g., noise) to be removed by digital and/or analog filters.

Bandlimited Cases: When X_(a)(jω) is bandlimited, only a finite number of terms in the summations of eqs. (2) and (8) need to be handled in the interval −π≦ωT≦π. We consider two different cases.

Case A (PR): Let X_(a)(t) be bandlimited according to eq. (18). In this case, the Nyquist criterion for sampling with an effective sampling frequency of 1/T without aliasing is fulfilled. Thus, x_(a)(t) can be retained if aliasing into the band −π≦ωT≦π is avoided.

Consider first x(n) in FIG. 2. From eq. (2), it is obvious that we have no aliasing in the region −π≦ωT≦π when X_(a)(jω) is bandlimited according to eq. (18). Consider next y(n) in FIG. 4. In the region −π≦ωT≦π, with X_(a)(jω) being bandlimited according to eq. (18), it is easy to verify that we only need to consider 2K₀+1 terms in eq. (8), for p=−K₀, −(K₀−1), . . . , K₀, with K₀ given by eq. (19).

PR is now obtained if eq. (20) prevails, where K₀ is given by eq. (19). In this case, x_(a)(t) can thus be retained from x(n) as well as y(n) provided that the system in FIG. 4 has PR.

Case B (RPR): Let X_(a)(t) be bandlimited according to eq. (21) and separated according to eq. (22) with the corresponding Fourier transforms given by eqs. (23), (24), and (25).

In this case, x_(a)(t) can not be retained but x_(a,low)(t) can be retained as long as aliasing into the band −ω₀T≦ωT≦ω₀T is avoided.

Consider first x(n) in FIG. 2. In the region −π≦ωT≦π, with X_(a)(jω) being bandlimited according to Eqs. (21) and (25), it is obvious that we only need to consider 3 terms in eq. (2), for r=−1, 0, 1. Further, in the region −ω₀T≦ωT≦ω₀T, with ω₀ being given by eq. (25), it is easy to verify that we only need to consider one term, for r=0. That is, aliasing into this band is automatically avoided. Consider next y(n) in FIG. 4. In the region −π≦ωT≦π, with X_(a)(jω) being bandlimited according to Eqs. (21) and (25), it is easy to verify that we only need to consider 2K₀+1 terms in eq. (8), for p=−K₀, −(K₀−1), . . . , K₀, with K₀ being given by eq. (26), where ┌x┐ stands for the smallest integer larger than or equal to x. Further, in the region −ω₀T≦ωT≦ω₀T, with ω₀ being given by eq. (25), it is readily verified that we only need to consider 2K₀+1 terms in eq. (8), for p=−K₀, −(K₀−1), . . . , K₀, where K₀ is given by eq. (27).

RPR is now obtained if eq. (28) is fulfilled, wherein K₀ is given by eq. (27) and A(jω) is some arbitrary function. In this case, X_(a,low)(t) can thus be retained from x(n) as well as y(n) provided that the system in FIG. 4 has RPR.

Nonuniform Sampling and Reconstruction

Let x_(k)(m), k=0, 1, . . . , N−1, be N subsequences obtained through sampling of x_(a)(t) at the time instances t=nMT+t_(k), i.e. as given by eq. (29). For M=N=2, x_(a)(t) is sampled according to FIG. 1b.

The subsequences x_(k)(m) can be obtained by sampling the output signals from the analysis filters in FIG. 4 if these filters are selected according to eq. (30). The analysis filter bank is in this case as shown in FIG. 5.

Combining Eqs. (9) and (30) gives us eq. (31).

Next, it is shown how to choose the synthesis filters in the bandlimited cases A and B (see previous section) so that PR and RPR, respectively, are obtained.

Case A (PR case): In this case X_(a)(t) is bandlimited according to eq. (18). Let G_(k)(e^(jωT)) be 2π-periodic filters given by eq. (32). From eqs. (31) and (32), eq. (33) is obtained. For PR it is required that V_(p)(jω) as given by eq. (33) fulfils eq. (20). That is, PR is obtained if eq. (34) is fulfilled.

Case B (RPR case): In this case X_(a)(t) is bandlimited according to eq. (21). Let G_(k)(e^(jωT)) be 2π-periodic filters given by eq. (35), where A_(k)(e^(jωT)) are some arbitrary complex functions. From eqs. (31) and (35) we obtain eq. (36), where A(jω) is given by eq. (37).

For RPR it is required that V_(p)(jω) as given by eq. (36) fulfils eq. (28). That is, RPR is obtained if, again eq. (34) is satisfied.

How to compute the a_(k)'s is next considered. For both PR and RPR (Cases A and B), eq. (34) must be fulfilled. This equation can be written in matrix form as eq. (38), where B is a (2K₀+1)×N matrix according to eq. (39), wherein the u_(k)'s are given by eq. (40). Further, a is a column vector with N elements and c is a column vector with 2K₀+1 elements according to eqs. (41) and (42), respectively, where T stands for the transpose (without complex conjugate). The a_(k)'s are the unknowns whereas the c_(k)'s are given in accordance with eq. (43).

Eq. (38) is a linear system of2K₀+1 equations with N unknown parameters a_(k). Hence, eq. (38) can be solved if 2K₀+1≦N. We distinguish two different cases.

Case 1: 2K₀+1=N. In this case, the number of unknowns equals the number of equations. The a_(k)'s can in this case be uniquely determined under the conditions stated by the following theorem.

Theorem 1: If B and c are as given by eqs. (39) and (42), respectively, 2K₀+1=N, and t_(k)≠t_(m)+MTr, k≠m, rεZ, then there exists a unique a satisfying eq. (38), and thereby also unique a_(k)'s satisfying eq. (34). Further, all the a_(k)'s in a are real-valued constants.

Proof: We first prove that there exists a unique solution. Since 2K₀+1=N, B is a square N×N matrix. If B is nonsingular, then a is uniquely determined by eq. (44), where B⁻¹ is the inverse of B. It thus suffices to show that B is nonsingular under the stated conditions. To this end, we first observe that B as given by eq. (39) can be written as in eq. (45), where A is given by eq. (46) and C is a diagonal matrix according to eq. (47).

The matrix A is a Vandermonde matrix. The necessary and sufficient condition for nonsingularity of A is therefore that the u_(k)'s are distinct, i.e., U_(k)≠u_(m), k≠m, which is the same condition as t_(k)≠t_(m)+MTr, k≠m, rεZ, due to eq. (40). Further, since the determinant of B is det B=det A det C, and |det C|=1, we obtain the relations as given in eq. (48). That is, B is nonsingular if and only if A is nonsingular. This proves that B is nonsingular and a unique solution a always exists under the stated conditions.

To prove that the a_(k)'s in a are real-valued constants we proceed as follows. Assume that we have the unique values a_(k) that satisfy eq. (34). Using eq. (40), eq. (34) can equivalently be written as eq. (49), where x* stands for the complex conjugate of x. From eq. (49) we get eq. (50). This shows that the values a_(k)* satisfy eq. (34) as well. However, since a_(k) are unique, it follows that they must be real-valued.

Case 2: 2K₀+1<N. In this case, the number of unknowns exceeds the number of equations. We can therefore impose L=N−2K₀−1 additional linear constraints among the a_(k)'s and still satisfy eq. (34). Here, we restrict ourselves to the case in which the L a_(k)'s for k=N−L+1, N−L+2, . . . , N, are fixed to some constants. This case covers the conventional time-interleaved ADCs with an even number of channels. Since L a_(k)'s are free we could of course set them to zero in the case of which the corresponding channels would be removed. In that sense, there is no need to consider the cases having an even number of channels. However, as we shall see below, it may be worth considering these cases in order to reduce the quantization noise at the output of the overall system.

The system of linear equations to be solved can here be written in matrix form as eq. (51) with {circumflex over (B)} being an N×N matrix, and a and ĉ being column vectors with N elements, according to eqs. (52), (53) and (54), respectively, where B is the (2K₀+1)×(2K₀+1) matrix as given by eq. (39), a_(u) and a_(fix) contain the (2K₀+1) unknowns and L fixed constants of a, respectively, c is the column vector with (2K₀+1) elements as given by eq. (43), S is an L×N matrix given by eq. (55), where S_(z) is an L×(2K₀+1) null matrix given by eq. (56), and S_(d) is a L×L diagonal matrix where the diagonal elements are equal to one, see eq. (57).

As in Case 1, the a_(k)'s can in Case 2 be uniquely determined under the conditions stated by the following theorem.

Theorem 2: If {circumflex over (B)} and ĉ are as given by eqs. (52) and (54), respectively, a_(fix) in eq. (53) contains L real fixed constants, 2K₀+1<N, and t_(k)≠t_(m)+MTr, k≠m, rεZ, then there exists a unique a satisfying eq. (51), and thereby also unique a_(k)'s satisfying eq. (34). Further, all the a_(k)'s in a are real-valued constants.

Proof: The proof follows that of Theorem 1. To prove the existence and uniqueness, it thus suffices to show that {circumflex over (B)} is nonsingular under the stated conditions since a then is uniquely determined by eq. (58).

To prove nonsingularity of {circumflex over (B)}, we observe that its determinant is given by eq. (59), where {tilde over (B)} is a (2K₀+1)×(2K₀+1) submatrix obtained from B by deleting L columns for k=N−L+1, N−L+2, . . . , N, i.e. as given in eq. (60). We know from the proof of Theorem 1 that det {tilde over (B)}≠0 and thus det {circumflex over (B)}≠0 under the stated conditions. This proves that {circumflex over (B)} is nonsingular and a unique solution always exists. The proof that the a_(k)'s in a are real-valued is done in the same manner as that of Theorem 1.

Time-interleaved ADCs and Their Generalizations

This section considers conventional time-interleaved ADCs and their generalizations. Consider first the case where N=M with t_(k) being given by eqs. (61) and (62).

Further, let the synthesis filters G_(k)(e^(jωT)) be given by eq. (32) with a_(k)=1, k=0, 1, . . . , M−1, c=1, and d=0, i.e., as in eq. (63). From eqs. (31) and (63) we obtain eq. (64).

Thus, PR is obtained. In this case we have a conventional time-interleaved ADC. The output sequence y(n) is here obtained by interleaving the x_(k)(m)'s.

In practice, Δt_(k) will however no longer be exactly zero. If Δt_(k) are known, the a_(k)'s can be computed according to eq. (44) if N is odd and 2K₀+1=N, or according to eq. (58) if 2K₀+1<N. In this case, PR can not be achieved since N=M and PR requires that K₀=M−1. Thus, neither 2K₀+1=N nor 2K₀+1 <N can be fulfilled. RPR can, on the other hand, be obtained. For this case, the following question arises: given N=M and K₀, what is the maximum value of ω₀T we can allow and still obtain RPR? It is readily established that to achieve RPR we must fulfill eq. (65). If 2K₀+1=N we get eq. (66).

Consider next the case where N≠M with t_(k) being given by eqs. (67) and (68). Further, let the synthesis filters G_(k)(e^(jωT)) be given by eq. (32) with a_(k)=M/N, k=0, 1, . . . , N−1, c=1, and d=0, i.e., as in eq. (69). From eqs. (31) and (69) we obtain eq. (70).

Thus, PR is obtained. In this case we have a system that can be viewed as a generalization of the time-interleaved ADCs. However, in this case we can no longer obtain the output sequence by interleaving the x_(k)(m)'s.

Again, Δt_(k) will in practice no longer be exactly zero. If Δt_(k) are known, the a_(k)'s can be computed according to eq. (44) if N is odd and 2K₀+1=N, or according to eq. (58) if 2K₀+1<N. As opposed to the M-channel case, we can here in the N-channel case achieve both PR and RPR by selecting K₀ according to eqs. (19) and (27), respectively, and of course choosing N so that 2K₀+1<N. To achieve RPR, for given M and K₀, ω₀T must again satisfy eq. (65). If 2K₀+1=N we get eq. (71). Hence, by increasing the number of channels we obtain RPR over a wider frequency region.

Error and Noise Analysis

Next an error analysis is provided. More precisely, we derive bounds on the errors in a and c, when B and a are replaced with B+ΔB and a+Δa, respectively. The errors in a are of interest as far as the quantization noise is concerned, as will become clear in the next section. The errors in c tell us how close to the ideal synthesis filters any practical filters must be in order to meet some prescribed allowable errors in c.

We will make use of the L_(∞)-norms as defined by eq. (72) for an N×1 (1×N) vector x with elements x_(i), and as defined by eq. (73) for an N×N matrix X with elements x_(ik).

Errors in a: Consider first Case 1 with 2K₀+1=N. Assume first that we have Ba=c for t_(k)=d_(k)T and a_(k). Assume next that t_(k)=d_(k)T and a_(k) are replaced with t_(k)=d_(k)T+Δt_(k) and a_(k)+Δa_(k), respectively, whereas c is kept fixed. This amounts to eq. (74). The matrix ΔB is an N×N matrix according to eq. (75), where Δb_(pk) and Δt_(pk) are given by eqs. (76) and (77), respectively.

Now, if eq. (78) is satisfied then it can be shown that eq. (79) holds. From eqs. (75)-(77) we get eq. (80).

We have B=AC and consequently B⁻¹=C⁻¹A⁻¹. Further, since A is here a DFT matrix, its inverse A⁻¹ is an IDFT matrix; hence ∥A⁻¹∥_(∞)=1. We also have ∥C⁻¹∥_(∞)=1 because C⁻¹ apparently is a diagonal matrix with diagonal elements u_(k) ^(K) ^(₀) where u_(k) are given by eq. (40). We thus have eq. (81), which, together with eq. (80), results in eq. (82). By using eqs. (79)-(82), and assuming ∥ΔB∥_(∞)∥B⁻¹∥_(∞)<<1, we finally obtain eq. (83).

Consider next Case 2 with 2K₀+1<N. This case is somewhat more difficult than Case 1 since we generally can not express {circumflex over (B)} as a product between a DFT matrix and a diagonal matrix. However, if we restrict ourselves to the time-interleaved ADCs and their generalizations, it is readily shown that we can rewrite eq. (51) as eq. (84), where B′ is an N×N matrix according to eq. (85) with u_(k) being given by eq. (40), and c′ is a column vector with N elements c_(k) according to eq. (86)

Clearly, we can express B′ as a product between a DFT matrix and a diagonal matrix. We will therefore end up with the same result as in Case 1, i.e., the bound in eq. (83).

Errors in c: Assume that we have Ba=c for t_(k)=d_(k)T and a_(k). Assume now that t_(k)=d_(k)T and a_(k) are replaced with t_(k)=d_(k)T+Δt_(pk) and a_(k)+Δa_(k), respectively. This amounts to eq. (87) from which we get eq. (88). In turn, from eq. (88) we obtain eq. (89). Using eqs. (39) and (75)-(77) we finally get eq. (90), which is useful in the design of the synthesis filters G_(k)(z)

Recall from above that the ideal filters should have the frequency responses a_(k) e^(−jwt) ^(_(k)) over the frequency range of interest [if c=1 and d=0 in eqs. (32) and (35)]. In practice, G_(k)(z) can of course only approximate the ideal responses. We can express the frequency responses of G_(k)(z) as eq. (91), where Δa_(k)(ωT) and Δt_(pk)(ωT) are the deviations from the ideal magnitude and phase responses, respectively. Given the allowable errors in c, and eqs. (90) and (91), it is thus easy to design G_(k)(z) so that the requirements are satisfied.

To analyze the noise variance at the output of the system in FIG. 4 it is convenient to represent the synthesis filter bank with its so called polyphase realization according to FIG. 6. The output sequence y(n) is obtained by interleaving the y_(i)(m)'s, i=0, 1, . . . , M−1. The transfer function of the output y(n) is given by eq. (92), where Y(z) is given by eq. (93), X(z), Y(z), and G^((p))(z) being defined in eqs. (94), (95), and (96), respectively. The G_(ik)(z)'s are the polyphase components of G_(k)(z) according to eq. (97).

As usual in noise analysis, the quantization errors are modeled as stationary white noise. Let x_(k)(m), k=0, 1, . . . , N−1, be uncorrelated white noise sources having zero mean and variances σ_(xk) ². Since G^((p))(z) describes a linear and time-invariant system, the outputs y_(i)(m), i=0, 1, . . . , M−1, are also stationary white noise with zero mean. However, the variances of y_(i)(m), denoted here by σ_(yi) ²(n), are in general different, even when σ_(xk) ² are equal. The outputs y_(i)(m) may also be correlated. The output noise y(n) will therefore generally not be stationary. Its variance, denoted here by σ_(y) ²(n), is thus time-variant. It is further periodic with period N since, obviously, eq. (98) holds.

We define the average quantization noise at the output in eq. (99). Given the synthesis filters G_(k)(z) and its polyphase components G_(ik)(z), (σ_(y) ²)_(av) can be computed as in eq. (100).

Now, let the synthesis filters be given by eq. (101) and all input variances σ_(xk) ² be equal according to eq. (102). Combining eqs. (100)-(102) gives us eq. (103).

A question that arises now is how to select the a_(k)'s so that (σ_(y) ²)_(av) as given by (103) is minimized subject to the constraint that PR or RPR is simultaneously achieved. Let us consider the problem as defined by eq. (104). The constraint in eq. (104) is one of those that must be satisfied to obtain PR or RPR. Since the sum of the a_(k)'s is M, the objective function to be minimized in eq. (104) can be rewritten as eq. (105). Hence, the solution to eq. (104) is obtained for a_(k)=M/N, k=0, 1, . . . , N−1, with the minimum value of (σ_(y) ²)_(av) as in eq. (106).

This shows that the selection a_(k)=M/N, for the time-interleaved ADCs and their generalizations minimizes the average quantization noise at the output.

In practice Δt_(k) will no longer be exactly zero which implies that a_(k) are replaced with a_(k)+Δa_(k). If Δa_(k) are small (and a_(k)>0) the average quantization noise is in this case given by eq. (107). With a_(k)=M/N, we obtain eq. (108). The quantity is obtained from eq. (83).

The present invention has considered the problem of reconstructing nonuniformly sampled bandlimited signals using digital filter banks. The overall system can be viewed as a generalization of the conventional time-interleaved ADCs, to which the former reduces as a special case. By generalizing the time-interleaved ADCs, it is possible to eliminate the errors that are introduced in practice due to time skew errors. We consider both perfect reconstruction (PR) and regionally perfect reconstruction (RPR) systems and it is shown how to obtain such systems by selecting the (ideal) digital filters properly.

The method for reconstructing a nonuniformly sampled bandlimited signal may be implemented in any suitable digital signal processing apparatus such as e.g. dedicated hardware, or a computer. The method is in the latter case performed by means of a computer program product comprising software code portions loaded into the internal memory of a suitable apparatus.

It will be obvious that the invention may be varied in a plurality of ways. Such variations are not to be regarded as a departure from the scope of the invention. All such modifications as would be obvious to one skilled in the art are intended to be included within the scope of the appended claims.

The list of equations is presented at the following pages.

List of Equations

x(n)=x _(a)(t)|_(t=nT) , −∞≦n≦∞  (1)

$\begin{matrix} {{X\left( ^{j\quad \omega \quad T} \right)} = {\frac{1}{T}{\sum\limits_{r = {- \infty}}^{\infty}{X_{a}\left( {{j\quad \omega} - {j\quad \frac{2\pi \quad r}{T}}} \right)}}}} & (2) \\ {{T_{1} = {MT}},\quad {f_{{sample},1} = \frac{f_{sample}}{M}}} & (3) \\ {{y(n)} = \left\{ \begin{matrix} {{x\left( {n/M} \right)},} & {{n = 0},{\pm M},{{\pm 2}M},\ldots} \\ {0,} & {otherwise} \end{matrix} \right.} & (4) \end{matrix}$

 Y(e ^(jωT))=X(e ^(jωT) ^(₁) )=X(e ^(jMωT))  (5)

$\begin{matrix} {{Y\left( ^{j\quad \omega \quad T} \right)} = {\sum\limits_{k = 0}^{N - 1}{{G_{k}\left( ^{j\quad \omega \quad T} \right)}{X_{k}\left( ^{j\quad M\quad \omega \quad T} \right)}}}} & (6) \\ \begin{matrix} {{X_{k}\left( ^{j\quad M\quad \omega \quad T} \right)} = {X_{k}\left( ^{j\quad \omega \quad T_{1}} \right)}} \\ {= {\frac{1}{T_{1}}{\sum\limits_{p = {- \infty}}^{\infty}{{H_{k}\left( {{j\quad \omega} - {j\quad \frac{2\pi \quad p}{T_{1}}}} \right)}{X_{a}\left( {{j\quad \omega} - {j\quad \frac{2\quad \pi \quad p}{T_{1}}}} \right)}}}}} \\ {= {\frac{1}{MT}{\sum\limits_{p = {- \infty}}^{\infty}{{H_{k}\left( {{j\quad \omega} - {j\quad \frac{2\pi \quad p}{MT}}} \right)}{X_{a}\left( {{j\quad \omega} - {j\quad \frac{2\quad \pi \quad p}{MT}}} \right)}}}}} \end{matrix} & (7) \\ {{{Y\left( ^{j\quad \omega \quad T} \right)} = {\frac{1}{T}{\sum\limits_{p = {- \infty}}^{\infty}{{V_{p}\left( {j\quad \omega} \right)}{X_{a}\left( {{j\quad \omega} - {j\quad \frac{2\pi \quad p}{MT}}} \right)}}}}}\quad} & (8) \\ {{V_{p}\left( {j\quad \omega} \right)} = {\frac{1}{M}{\sum\limits_{k = 0}^{N - 1}{{G_{k}\left( ^{j\quad \omega \quad T} \right)}{H_{k}\left( {{j\quad \omega} - {j\quad \frac{2\pi \quad p}{MT}}} \right)}}}}} & (9) \end{matrix}$

 Y(e ^(jωT))=ce ^(−jdωT) X(e ^(jωT)), |ωT|≦π  (10)

$\begin{matrix} {{V_{p}\left( {j\quad \omega} \right)} = \left\{ \begin{matrix} {{c\quad ^{{- j}\quad d\quad \omega \quad T}},} & {{p = {rM}},\left| \omega \middle| {\leq {\pi/T}} \right.} \\ {0,} & {{p \neq {rM}},\left| \omega \middle| {\leq {\pi/T}} \right.} \end{matrix} \right.} & (11) \end{matrix}$

 x(n)=x _(low)(n)+x _(high)(n)

y(n)=y _(low)(n)+y _(high)(n)  (12)

X(e ^(jωT))=X _(low)(e ^(jωT))+X _(high)(e ^(jωT))

Y(e ^(jωT))=Y _(low)(e ^(jωT))+Y _(high)(e ^(jωT))  (13)

X _(low)(e ^(jωT))=0, ω₀ T≦|ωT|≦π

X _(high)(e ^(jωT))=0, |ωT|≦ω ₀ T

Y _(low)(e ^(jωT))=0, ω₀ T≦|ωT|≦π

Y _(high)(e ^(jωT))=0, |ωT|≦ω ₀ T  (14)

Y(e ^(jωT))=ce ^(−jdωT) X(e ^(jωT)), |ωT|≦ω ₀ T  (15)

Y _(low)(e ^(jωT))=ce ^(−jdωT) X _(low)(e ^(jωT)), |ωT|≦π  (16)

$\begin{matrix} {{V_{p}\left( {j\quad \omega} \right)} = \left\{ \begin{matrix} {{c\quad ^{{- j}\quad d\quad \omega \quad T}},} & {{p = {rM}},\left| \omega \middle| {\leq \omega_{0}} \right.} \\ {0,} & {{p \neq {rM}},\left| \omega \middle| {\leq \omega_{0}} \right.} \end{matrix} \right.} & (17) \end{matrix}$

 X _(a)(jω)=0, |ω|≧π/T  (18)

 K ₀ =M−1  (19)

$\begin{matrix} {{V_{p}\left( {j\quad \omega} \right)} = \left\{ \begin{matrix} {{c\quad ^{{- j}\quad d\quad \omega \quad T}},} & {{p = 0},\left| \omega \middle| {< \pi} \right.} \\ {0,} & {{|p| = 1},2,\ldots,K_{0},{0 \leq \omega \leq \pi}} \end{matrix} \right.} & (20) \end{matrix}$

 X _(a)(jω)=0, |ω|≧ω₁  (21)

x _(a)(t)=x _(a,low)(t)+x _(a,high)(t)  (22)

X _(a)(jω)=X _(a,low)(jω)+X _(a,high)(jω)  (23)

X _(low)(jω)=0, |ω|>ω₀

X _(high)(jω)=0, |ω|≦ω₀, |ω|≧ω₁  (24)

0<ω₀<ω₁, ω₀+ω₁≦2π/T  (25)

$\begin{matrix} {K_{0} = {\left\lceil \frac{M\left( {\pi + {\omega_{1}T}} \right)}{2\pi} \right\rceil - 1}} & (26) \\ {K_{0} = {\left\lceil \frac{M\left( {{\omega_{0}T} + {\omega_{1}T}} \right)}{2\pi} \right\rceil - 1}} & (27) \\ {{V_{p}\left( {j\quad \omega} \right)} = \left\{ \begin{matrix} {{c\quad ^{{- j}\quad d\quad \omega \quad T}},} & {{p = 0},\left| \omega \middle| {\leq \omega_{0}} \right.} \\ {{A\left( {j\quad \omega} \right)},} & {{p = 0},\left. {\omega_{0} \leq} \middle| \omega \middle| {\leq {\pi/T}} \right.} \\ {0,} & {{|p| = 1},2,\ldots,K_{0},\left| \omega \middle| {\leq \omega_{0}} \right.} \end{matrix} \right.} & (28) \end{matrix}$

 x _(k)(m)=x(nMT+t _(k)), k=0, 1, . . . , N−1  (29)

H _(k)(s)=e ^(st) ^(_(k)) , k=0, 1, . . . , N−1  (30)

$\begin{matrix} {{V_{p}\left( {j\quad \omega} \right)} = {\frac{1}{M}{\sum\limits_{k = 0}^{N - 1}{{G_{k}\left( ^{j\quad \omega \quad T} \right)}^{j\quad {({\omega - \frac{2\pi \quad p}{MT}})}t_{k}}}}}} & (31) \end{matrix}$

 G _(k)(e ^(jωT))=a _(k) ce ^(−jω(t) ^(_(k)) ^(+dT)) , |ωT|<π  (32)

$\begin{matrix} {{V_{p}\left( {j\quad \omega} \right)} = {\frac{1}{M}c\quad ^{{- j}\quad d\quad \omega \quad T}{\sum\limits_{k = 0}^{N - 1}{a_{k}^{{- j}\quad \frac{2\quad p}{MT}t_{k}}}}}} & (33) \\ {{\sum\limits_{k = 0}^{N - 1}{a_{k}^{{- j}\quad \frac{2\quad p}{MT}t_{k}}}} = \left\{ \begin{matrix} {M,} & {p = 0} \\ {0,} & {{|p| = 1},2,\ldots,K_{0}} \end{matrix} \right.} & (34) \\ {{G_{k}\left( ^{j\quad \omega \quad T} \right)} = \left\{ \begin{matrix} {{a_{k}c\quad ^{{- {j\omega}}\quad {({t_{k} + {d\quad T}})}}},} & \left| {\omega \quad T} \middle| {\leq {\omega_{0}T}} \right. \\ {{a_{k}{A_{k}\left( ^{j\quad \omega \quad T} \right)}},} & \left. {{\omega_{0}T} <} \middle| {\omega \quad T} \middle| {\leq \pi} \right. \end{matrix} \right.} & (35) \\ {{V_{p}\left( {j\quad \omega} \right)} = \left\{ \begin{matrix} {{\frac{1}{M}c\quad ^{{- j}\quad d\quad \omega \quad T}{\sum\limits_{k = 0}^{N - 1}{a_{k}^{{- j}\quad \frac{2\quad p}{MT}t_{k}}}}},} & \left| {\omega \quad T} \middle| {\leq {\omega_{0}T}} \right. \\ {{A\left( {j\quad \omega} \right)},} & \left. {{\omega_{0}T} <} \middle| {\omega \quad T} \middle| {\leq \pi} \right. \end{matrix} \right.} & (36) \\ {{A\left( {j\quad \omega} \right)} = {\frac{1}{M}{\sum\limits_{k = 0}^{N - 1}{a_{k}{A_{k}\left( ^{j\quad \omega \quad T} \right)}^{j\quad {({\omega - \frac{2\pi \quad p}{MT}})}t_{k}}}}}} & (37) \end{matrix}$

 Ba=c  (38)

$\begin{matrix} {B = \begin{bmatrix} u_{0}^{- K_{0}} & u_{1}^{- K_{0}} & \cdots & u_{N - 1}^{- K_{0}} \\ u_{0}^{- {({K_{0} - 1})}} & u_{1}^{- {({K_{0} - 1})}} & \cdots & u_{N - 1}^{- {({K_{0} - 1})}} \\ \vdots & \vdots & \quad & \vdots \\ u_{0}^{K_{0}} & u_{1}^{K_{0}} & \cdots & u_{N - 1}^{K_{0}} \end{bmatrix}} & (39) \\ {u_{k} = ^{{- j}\quad \frac{2\quad }{MT}t_{k}}} & (40) \end{matrix}$

 a=[a ₀ a ₁ . . . a _(N−1)]^(T)  (41)

c=[c ₀ c ₁ . . . c _(2K) ₀ ]^(T)  (42)

$\begin{matrix} {c_{k} = \left\{ \begin{matrix} {M,} & {k = K_{0}} \\ {0,} & {{k = 0},1,\ldots,{2K_{0}},{k \neq K_{0}}} \end{matrix} \right.} & (43) \end{matrix}$

 a=B ⁻¹ c  (44)

B=AC  (45)

$\begin{matrix} {A = \begin{bmatrix} 1 & 1 & \cdots & 1 \\ u_{0} & u_{1} & \cdots & u_{N - 1} \\ \vdots & \vdots & \quad & \vdots \\ u_{0}^{2K_{0}} & u_{1}^{2K_{0}} & \cdots & u_{N - 1}^{2K_{0}} \end{bmatrix}} & (46) \\ {C = {{diag}\begin{bmatrix} u_{0}^{- K_{0}} & u_{1}^{- K_{0}} & \cdots & u_{N - 1}^{- K_{0}} \end{bmatrix}}} & (47) \end{matrix}$

 det A≠0det B≠0

det A=0det B=0  (48)

$\begin{matrix} \begin{matrix} {{{{\sum\limits_{k = 0}^{N - 1}\quad a_{k}} = \quad M},}\quad} & {\quad {p = 0}} \\ {{{\underset{k = 0}{\sum\limits^{N - 1}}\quad {a_{k}\left\lbrack u_{k}^{p} \right\rbrack}^{*}} = \quad {{\sum\limits_{k = 0}^{N - 1}\quad {a_{k}u_{k}^{p}}} = 0}},} & {\quad {{p = 1},2,\ldots \quad,K_{0}}} \end{matrix} & (49) \\ \begin{matrix} {{{\sum\limits_{k = 0}^{N - 1}\quad a_{k}^{*}} = \quad M},} & {\quad {p = 0}} \\ {{{\underset{k = 0}{\sum\limits^{N - 1}}\quad {a_{k}^{*}u_{k}^{p}}} = \quad {{\sum\limits_{k = 0}^{N - 1}\quad {a_{k}^{*}\left\lbrack u_{k}^{p} \right\rbrack}^{*}} = 0}},} & {\quad {{p = 1},2,\ldots \quad,K_{0}}} \end{matrix} & (50) \end{matrix}$

 {circumflex over (B)}a=ĉ  (51)

$\begin{matrix} {\hat{B} = \begin{bmatrix} B \\ S \end{bmatrix}} & (52) \end{matrix}$

 a=[a _(u) a _(fix)]^(T)  (53)

ĉ=[c a _(fix)]^(T)  (54)

S=[S _(z) S _(d)]  (55)

$\begin{matrix} {{S_{z} = \begin{bmatrix} 0 & 0 & \cdots & 0 \\ 0 & 0 & \cdots & 0 \\ \vdots & \vdots & \quad & \vdots \\ 0 & 0 & \cdots & 0 \end{bmatrix}},} & (56) \end{matrix}$

 S _(d)=diag[1 1 . . . 1].  (57)

a={circumflex over (B)} ⁻¹ ĉ  (58)

$\begin{matrix} {{\det \hat{B}} = {{\det \overset{\sim}{B}{\prod\limits_{l = 0}^{L - 1}\quad S_{d,{ll}}}} = {\det \overset{\sim}{B}}}} & (59) \\ {\overset{\sim}{B} = \begin{bmatrix} u_{0}^{- K_{0}} & u_{1}^{- K_{0}} & \cdots & u_{2K_{0}}^{- K_{0}} \\ u_{0}^{- {({K_{0} - 1})}} & u_{1}^{- {({K_{0} - 1})}} & \cdots & u_{2K_{0}}^{- {({K_{0} - 1})}} \\ \vdots & \vdots & \quad & \vdots \\ u_{0}^{K_{0}} & u_{1}^{K_{0}} & \cdots & u_{2K_{0}}^{K_{0}} \end{bmatrix}} & (60) \end{matrix}$

 t _(k) =d _(k) T+Δt _(k) , k=0, 1, . . . , M−1  (61)

d _(k) =k, k=0, 1, . . . , M−1

Δt _(k)=0, k=0, 1, . . . , M−1  (62)

G _(k)(e^(jωT))=e ^(−jkωT) , |ωT|<π  (63)

$\begin{matrix} {{V_{p}({j\omega})} = {{\frac{1}{M}{\sum\limits_{k = 0}^{M - 1}\quad ^{{- j}2\pi \quad p\quad \frac{k}{M}}}} = \left\{ \begin{matrix} {1,} & {p = 0} \\ {0,} & {p \neq 0} \end{matrix} \right.}} & (64) \\ {{\omega_{0}T} \leq {\frac{2{\pi \left( {K_{0} + 1} \right)}}{M} - {\omega_{1}T}}} & (65) \\ {{{\omega_{0}T} \leq {\frac{\pi \left( {M + 1} \right)}{M} - {\omega_{1}T}}} = {\frac{\pi}{M} + \pi - {\omega_{1}T}}} & (66) \end{matrix}$

 t _(k) =d _(k) T+Δt _(k) , k=0, 1, . . . , N=1  (67)

$\begin{matrix} {{d_{k} = \frac{kM}{N}},\quad {k = 0},1,\ldots \quad,{N - 1}} & (68) \end{matrix}$

 Δt _(k)=0, k=0, 1, . . . , N−1  (68)

$\begin{matrix} {{{G_{k}\left( ^{{j\omega}\quad T} \right)} = {\frac{M}{N}^{- \frac{{- j}\quad {Mk}\quad \omega \quad T}{N}}}},\quad {{{\omega \quad T}} < \pi}} & (69) \\ {{V_{p}({j\omega})} = {{\frac{1}{N}{\sum\limits_{k = 0}^{N - 1}\quad ^{{- j}2\pi \quad p\quad \frac{k}{N}}}} = \left\{ \begin{matrix} {1,} & {p = 0} \\ {0,} & {p \neq 0} \end{matrix} \right.}} & (70) \\ {{\omega_{0}T} \leq {\frac{\pi \quad \left( {N + 1} \right)}{M} - {\omega_{1}T}}} & (71) \end{matrix}$

 ∥x∥ _(∞)=max|x _(i)|, 0≦i≦N−1  (72)

$\begin{matrix} {{{{}X{}_{\infty}} = {\max {\sum\limits_{k = 0}^{N - 1}\quad {x_{ik}}}}},\quad {0 \leq i \leq {N - 1}}} & (73) \end{matrix}$

 (B+ΔB)(a+Δa)=c.  (74)

$\begin{matrix} {{\Delta \quad B} = \begin{bmatrix} {\Delta \quad b_{{- K_{0}},0}} & {\Delta \quad b_{{- K_{0}},1}} & \cdots & {\Delta \quad b_{{- K_{0}},{N - 1}}} \\ {\Delta \quad b_{{- {({K_{0} - 1})}},0}} & {\Delta \quad b_{{- {({K_{0} - 1})}},1}} & \cdots & {\Delta \quad b_{{- {({K_{0} - 1})}},{N - 1}}} \\ \vdots & \vdots & \quad & \vdots \\ {\Delta \quad b_{K_{0},0}} & {\Delta \quad b_{K_{0},1}} & \cdots & {\Delta \quad b_{K_{0},{N - 1}}} \end{bmatrix}} & (75) \\ {{\Delta \quad b_{p\quad k}} = {^{j2\pi \quad p\quad \frac{k}{M}}\left( {^{{j\Delta}\quad t_{p\quad k}} - 1} \right)}} & (76) \\ {{\Delta \quad t_{p\quad k}} = {\frac{2\pi \quad p}{MT}\Delta \quad t_{k}}} & (77) \end{matrix}$

 ∥ΔB∥ _(∞) ·∥B ⁻¹∥_(∞)<1  (78)

$\begin{matrix} {\frac{{}\Delta \quad a{}_{\infty}}{{}a{}_{\infty}} \leq \frac{{}\Delta \quad B{{}_{\infty} \cdot {}}B^{- 1}{}_{\infty}}{1 - {{}\Delta \quad B{{}_{\infty} \cdot {}}B^{- 1}{}_{\infty}}}} & (79) \\ \begin{matrix} {{{}\Delta \quad B{}_{\infty}} = \quad {{\max {\sum\limits_{k = 0}^{N - 1}\quad {{\Delta \quad b_{p\quad k}}}}} \approx {\max {\sum\limits_{k = 0}^{N - 1}\quad {{\Delta \quad t_{p\quad k}}}}} \leq}} \\ {\quad {{\frac{{N\left( {N - 1} \right)}\pi}{M} \cdot \max}{\left\{ \frac{{\Delta \quad t_{k}}}{T} \right\}.}}} \end{matrix} & (80) \end{matrix}$

 ∥B ⁻¹∥_(∞) ≦∥C ⁻¹∥_(∞) ·∥A ⁻¹∥_(∞)=1  (81)

$\begin{matrix} {{{}\Delta \quad B{{}_{\infty} \cdot {}}B^{- 1}{}_{\infty}} \lesssim {{\frac{{N\left( {N - 1} \right)}\pi}{M} \cdot \max}{\left\{ \frac{{\Delta \quad t_{k}}}{T} \right\}.}}} & (82) \\ {{{}\Delta \quad a{}_{\infty}} \lesssim {{}a{}_{\infty}{N\left( {N - 1} \right)}{\frac{\pi}{M} \cdot \max}{\left\{ \frac{{\Delta \quad t_{k}}}{T} \right\}.}}} & (83) \end{matrix}$

 B′a=c′  (84)

$\begin{matrix} {B^{\prime} = \begin{bmatrix} u_{0}^{- K_{0}} & u_{1}^{- K_{0}} & \cdots & u_{N - 1}^{- K_{0}} \\ u_{0}^{- {({K_{0} - 1})}} & u_{1}^{- {({K_{0} - 1})}} & \cdots & u_{N - 1}^{- {({K_{0} - 1})}} \\ \vdots & \vdots & \quad & \vdots \\ u_{0}^{N - K_{0} - 1} & u_{1}^{N - K_{0} - 1} & \cdots & u_{N - 1}^{N - K_{0} - 1} \end{bmatrix}} & (85) \\ {c_{k} = \left\{ \begin{matrix} {M,} & {k = K_{0}} & \quad \\ {0,} & {{k = 0},1,\ldots \quad,{N - 1},} & {k \neq K_{0}} \end{matrix} \right.} & (86) \end{matrix}$

 (B+ΔB)(a+Δa)=c+Δc  (87)

Δc=BΔa+ΔBa+ΔBΔa  (88)

∥Δc∥ _(∞) ∥B∥ _(∞) ≦∥Δa∥ _(∞) +∥ΔB∥ _(∞) ∥a∥ _(∞) +∥ΔB∥ _(∞) ∥Δa∥ _(∞)  (89)

∥Δc∥ _(∞) <Nmax{|Δa _(k) |}+Nmax{|Δt _(pk)|}max{|a _(k) |}+Nmax{|Δt _(pk)|}max{|Δa _(k) |}≈N(max{|Δa _(k)|}+max{|Δt _(pk)|}max{|a _(k)|})  (90)

G _(k)(e^(jωT))=[a _(k) +Δa _(k)(ωT)]e ^(−j[ωt) ^(_(k)) ^(+Δt) ^(_(pk)) ^((ωT)])  (91)

$\begin{matrix} {{Y(z)} = {\sum\limits_{i = 0}^{M - 1}\quad {z^{- i}{Y_{i}\left( z^{M} \right)}}}} & (92) \end{matrix}$

 Y(z)=G ^((p))(z)X(z)  (93)

X(z)=[X ₀(z) X ₁(z). . . X _(N−1)(z)]^(T)  (94)

Y(z)=[Y ₀(z) Y ₁(z). . . Y _(N−1)(z)]^(T)  (95)

$\begin{matrix} {{G^{(p)}(z)} = \begin{bmatrix} {G_{00}(z)} & {G_{01}(z)} & \cdots & {G_{0,{N - 1}}(z)} \\ {G_{10}(z)} & {G_{11}(z)} & \cdots & {G_{1,{N - 1}}(z)} \\ \vdots & \vdots & \quad & \vdots \\ {G_{{M - 1},0}(z)} & {G_{{M - 1},1}(z)} & \cdots & {G_{{M - 1},{N - 1}}(z)} \end{bmatrix}} & (96) \\ {{G_{k}(z)} = {\sum\limits_{i = 0}^{M - 1}\quad {z^{- i}{G_{ik}\left( z^{M} \right)}}}} & (97) \end{matrix}$

 σ_(y) ²(nM+i)=σ_(y) _(i) ²  (98)

$\begin{matrix} {\left( \sigma_{y}^{2} \right)_{av} = {\frac{1}{M}{\sum\limits_{i = 0}^{M - 1}\quad \sigma_{y_{i}}^{2}}}} & (99) \\ \begin{matrix} {\left( \sigma_{y}^{2} \right)_{av} = \quad {\frac{1}{M}{\sum\limits_{i = 0}^{M - 1}\quad \sigma_{y_{i}}^{2}}}} \\ {= \quad {\frac{1}{M}{\sum\limits_{i = 0}^{M - 1}\quad {\sum\limits_{k = 0}^{N - 1}\quad {\sigma_{x_{k}}^{2}{\sum\limits_{n = {- \infty}}^{\infty}\quad {{g_{ik}(n)}}^{2}}}}}}} \\ {= \quad {\frac{1}{M}{\sum\limits_{k = 0}^{N - 1}\quad {\sigma_{x_{k}}^{2}{\sum\limits_{i = 0}^{M - 1}\quad {\sum\limits_{n = {- \infty}}^{\infty}\quad {{g_{ik}(n)}}^{2}}}}}}} \\ {= \quad {\frac{1}{M}{\sum\limits_{k = 0}^{N - 1}\quad {\sigma_{x_{k}}^{2}{\sum\limits_{n = {- \infty}}^{\infty}\quad {{g_{k}(n)}}^{2}}}}}} \\ {= \quad {\frac{1}{M}{\sum\limits_{k = 0}^{N - 1}\quad {\sigma_{x_{k}}^{2}\frac{1}{2\pi}{\int_{- \pi}^{\pi}{{{G_{k}\left( ^{{j\omega}\quad T} \right)}}^{2}\quad {\omega}\quad T}}}}}} \end{matrix} & (100) \\ {{G_{k}\left( ^{{j\omega}\quad T} \right)} = \left\{ \begin{matrix} {a_{K}^{{{- j}\quad {Mk}\quad \omega \quad {T/N}},}} & {{{{\omega \quad T}} < {\omega_{c}T}}\quad} \\ {{0,}\quad} & {{\omega_{c}T} \leq {{\omega \quad T}} \leq \pi} \end{matrix} \right.} & (101) \end{matrix}$

 σ_(x) _(k) ²=σ_(x) ².  (102)

$\begin{matrix} {\left( \sigma_{y}^{2} \right)_{av} = {\frac{\omega_{c}T}{M\quad \pi}\sigma_{x}^{2}{\sum\limits_{k = 0}^{N - 1}\quad a_{k}^{2}}}} & (103) \\ {{{minimize}\quad {\sum\limits_{k = 0}^{N - 1}\quad {a_{k}^{2}\quad {subject}\quad {to}\quad {\sum\limits_{k = 0}^{N - 1}\quad a_{k}}}}} = M} & (104) \\ \begin{matrix} {{\sum\limits_{k = 0}^{N - 1}\quad a_{k}^{2}} = \quad {\left( {\sum\limits_{k = 0}^{N - 1}\quad a_{k}} \right)^{2} + {\sum\limits_{k = 0}^{N - 2}\quad {\sum\limits_{q = {k + 1}}^{N - 1}\quad \left( {a_{k} - a_{q}} \right)^{2}}}}} \\ {= \quad {M^{2} + {\sum\limits_{k = 0}^{N - 2}\quad {\sum\limits_{q = {k + 1}}^{N - 1}\quad \left( {a_{k} - a_{q}} \right)^{2}}}}} \end{matrix} & (105) \\ {\left( \sigma_{y}^{2} \right)_{{av},{m\quad i\quad n}} = {\frac{M\quad \omega_{c}T}{N\quad \pi}\sigma_{x}^{2}}} & (106) \\ \begin{matrix} {\left( \sigma_{y}^{2} \right)_{av} = \quad {\frac{\omega_{c}T}{M\quad \pi}\sigma_{x}^{2}{\sum\limits_{k = 0}^{N - 1}\quad \left( {a_{k} + {\Delta \quad a_{k}}} \right)^{2}}}} \\ {\approx \quad {\frac{\omega_{c}T}{M\quad \pi}\sigma_{x}^{2}{\sum\limits_{k = 0}^{N - 1}\quad \left( {a_{k}^{2} + {2a_{k}\Delta \quad a_{k}}} \right)}} \leq} \\ {\quad {\frac{\omega_{c}T}{M\quad \pi}\sigma_{x}^{2}{\sum\limits_{k = 0}^{N - 1}\quad \left( {a_{k}^{2} + {2a_{k}{{\Delta \quad a_{k}}}_{m\quad a\quad x}}} \right)}}} \end{matrix} & (107) \\ \begin{matrix} {\left( \sigma_{y}^{2} \right)_{av} \leq \quad {\frac{\omega_{c}T}{M\quad \pi}{\sigma_{x}^{2}\left( {\frac{M^{2}}{N} + {2M{{\Delta \quad a_{k}}}_{m\quad a\quad x}}} \right)}}} \\ {= \quad {\frac{M\quad \omega_{c}T}{N\quad \pi}{\sigma_{x}^{2}\left( {1 + \frac{2N{{\Delta \quad a_{k}}}_{m\quad a\quad x}}{M}} \right)}}} \\ {= \quad {\left( \sigma_{y}^{2} \right)_{{av},{m\quad i\quad n}} \cdot \left( {1 + \frac{2N{{\Delta \quad a_{k}}}_{m\quad a\quad x}}{M}} \right)}} \end{matrix} & (108) \end{matrix}$ 

What is claimed is:
 1. A method for reconstruction of a nonuniformly sampled bandlimited analog signal x_(a)(t), said nonuniformly sampled signal comprising N subsequences x_(k)(m), k=0, 1, . . . , N−1, N≧2, obtained through sampling at a sampling rate of 1/(MT) according to x_(k)(m)=x_(a)(nMT+t_(k)), where M is a positive integer, and t_(k)=kMT/N+Δt_(k), Δt_(k) being different from zero, said method comprising forming a new sequence y(n) from said N subsequences x_(k)(m) such that y(n) at least contains the same information as x(n)=x_(a)(nT), i.e. x_(a)(t) sampled with a sampling rate of 1/T, in a frequency region lower than ω₀, ω₀ being a predetermined limit frequency, by means of: (i) upsampling each of said N subsequences x_(k)(m), k=0, 1, . . . , N−1, by a factor M; (ii) filtering each of said upsampled N subsequences x_(k)(m), k=0, 1, . . . , N−1, by a respective digital filter; and (iii) adding said N digitally filtered subsequences to form y(n).
 2. The method as claimed in claim 1 wherein the respective digital filter is a fractional delay filter and has a frequency response G_(k)=a_(k)e^((−jωsT)), k=0, 1, . . . , N−1, in the frequency band |ωT|≦ω₀T, a_(k) being a constant and s being different from an integer.
 3. The method as claimed in claim 2 wherein s=d+t_(k), d being an integer.
 4. The method as claimed in claim 2 wherein the respective fractional delay filter has a frequency response G_(k)=a_(k)A_(k)(e^(jωT)), k=0, 1, . . . , N−1, in the frequency band ω₀T<|ωT|≧π, where A_(k)(e^(jωT)) is an arbitrary complex function.
 5. The method as claimed in claim 2 wherein the a_(k)'s are selected such that ${\sum\limits_{k = 0}^{N - 1}\quad {a_{k}^{{- j}\frac{2\quad \pi \quad p}{MT}t_{k}}}} = \left\{ \begin{matrix} {M,} & {p = 0} \\ {0,} & {{{p} = 1},2,\ldots \quad,K_{0}} \end{matrix} \right.$

is fulfilled, wherein K₀ is given by ${K_{0} = {\left\lceil \frac{M\left( {{\omega_{0}T} + {\omega_{1}T}} \right)}{2\pi} \right\rceil - 1}},$

wherein ┌x┐ should be read as the smallest integer larger than or equal to x, and [−ω₁, ω₁] is the frequency band wherein said bandlimited analog signal x_(a)(t) is found.
 6. The method as claimed in claim 5 wherein the a_(k)'s are calculated as a=B ⁻¹ c, a being the a_(k)'s in vector form given by a=[a ₀ a ₁ . . . a _(N−1)]^(T), B⁻¹ being the inverse of B as given by ${B = \begin{bmatrix} u_{0}^{- K_{0}} & u_{1}^{- K_{0}} & \cdots & u_{N - 1}^{- K_{0}} \\ u_{0}^{- {({K_{0} - 1})}} & u_{1}^{- {({K_{0} - 1})}} & \cdots & u_{N - 1}^{- {({K_{0} - 1})}} \\ \vdots & \vdots & \quad & \vdots \\ u_{0}^{K_{0}} & u_{1}^{K_{0}} & \cdots & u_{N - 1}^{K_{0}} \end{bmatrix}},$

 wherein ${u_{k} = ^{{- j}\frac{2\pi}{MT}t_{k}}},$

 and c being c=[c ₀ c ₁ . . . c _(2K) ₀ ]^(T),  wherein $c_{k} = \left\{ {\begin{matrix} {M,} & {k = K_{0}} & \quad \\ {0,} & {{k = 0},1,\ldots \quad,{2K_{0}},} & {k \neq K_{0}} \end{matrix},} \right.$

provided that 2K₀+1=N.
 7. The method as claimed in claim 5 wherein the a_(k)'s are calculated as a={circumflex over (B)} ⁻¹ ĉ, a being defined as a=[a _(u) a _(fix)]^(T), wherein a_(u) and a_(fix) contain (2K₀+1) unknown a_(k)'s and L=N−2K₀−1 fixed constant a_(k)'s, {circumflex over (B)}⁻¹ being the inverse of {circumflex over (B)}as given by ${\hat{B} = \begin{bmatrix} B \\ S \end{bmatrix}},$

wherein B is given by ${B = \begin{bmatrix} u_{0}^{- K_{0}} & u_{1}^{- K_{0}} & \cdots & u_{N - 1}^{- K_{0}} \\ u_{0}^{- {({K_{0} - 1})}} & u_{1}^{- {({K_{0} - 1})}} & \cdots & u_{N - 1}^{- {({K_{0} - 1})}} \\ \vdots & \vdots & \quad & \vdots \\ u_{0}^{K_{0}} & u_{1}^{K_{0}} & \cdots & u_{N - 1}^{K_{0}} \end{bmatrix}},$

 wherein ${u_{k} = ^{{- j}\frac{2\pi}{MT}t_{k}}},$

S is given by S=[S _(z) S _(d)],  wherein ${S_{z} = \begin{bmatrix} 0 & 0 & \cdots & 0 \\ 0 & 0 & \cdots & 0 \\ \vdots & \vdots & \quad & \vdots \\ 0 & 0 & \cdots & 0 \end{bmatrix}},$

 and S _(d)=diag[1 1 . . . 1],  and ĉ being ĉ=[c a _(fix)]^(T),  wherein c is given by c=[c ₀ c ₁ . . . c _(2K) ₀ ]^(T),  wherein $c_{k} = \left\{ {\begin{matrix} {M,} & {k = K_{0}} & \quad \\ {0,} & {{k = 0},1,\ldots \quad,{2K_{0}},} & {k \neq K_{0}} \end{matrix},} \right.$

provided that 2K₀+1<N.
 8. The method as claimed in claim 7 wherein L=N−2K₀−1 of the a_(k)'s are calculated a_(k)=M/N, k=N−L+1, N−L+2, . . . , N.
 9. The method as claimed in claim 7 wherein L=N−2K₀−1 of the ak's are calculated a_(k)=0, k=N−L+1, N−L+2, . . . , N.
 10. The method as claimed in claim 2 wherein N=M.
 11. The method as claimed in claim 2 wherein N≠M.
 12. The method as claimed in claim 10, wherein ω₀ is selected according to ${\omega_{0}T} \leq {\frac{2{\pi \left( {K_{0} + 1} \right)}}{M} - {\omega_{1}T}}$

K₀ being given by $K_{0} = {\left\lceil \frac{M\left( {{\omega_{0}T} + {\omega_{1}T}} \right)}{2\pi} \right\rceil - 1}$

wherein ┌x┐ should be read as the smallest integer larger than or equal to x and [−ω₁, ω₁] is the frequency band wherein said bandlimited analog signal x_(a)(t) is found.
 13. The method as claimed in claim 1 wherein the respective digital filter is a fractional delay filter and has a frequency response G_(k)=a_(k)e^((−jωsT)), k=0, 1, . . . , N−1, in the frequency band |ωT|≦π, a_(k) being a constant and s being different from an integer, and thus said new sequence y(n) formed being exactly equal to x(n).
 14. The method as claimed in claim 13 wherein s=d+t_(k), d being an integer.
 15. The method as claimed in claim 13 wherein the a_(k)'s are selected such that ${\sum\limits_{k = 0}^{N - 1}\quad {a_{k}^{{- j}\frac{2\pi \quad p}{MT}t_{k}}}} = \left\{ \begin{matrix} {M,} & {p = 0} \\ {0,} & {{{p} = 1},2,\ldots \quad,K_{0}} \end{matrix} \right.$

is fulfilled, wherein K₀ is given by K ₀ =M−1.
 16. The method as claimed in claim 15 wherein the a_(k)'s are calculated as a=B ⁻¹ c, a being the a_(k)'s in vector form given by a=[a ₀ a ₁ . . . a _(N−1)]^(T), B⁻¹ being the inverse of B as given by ${B = \begin{bmatrix} u_{0}^{- K_{0}} & u_{1}^{- K_{0}} & \cdots & u_{N - 1}^{- K_{0}} \\ u_{0}^{- {({K_{0} - 1})}} & u_{1}^{- {({K_{0} - 1})}} & \cdots & u_{N - 1}^{- {({K_{0} - 1})}} \\ \vdots & \vdots & \quad & \vdots \\ u_{0}^{K_{0}} & u_{1}^{K_{0}} & \cdots & u_{N - 1}^{K_{0}} \end{bmatrix}},$

 wherein ${u_{k} = ^{{- j}\frac{2}{MT}t_{k}}},$

and c being c=[c ₀ c ₁ . . .c _(2K) ₀ ]^(T),  wherein $c_{k} = \left\{ \begin{matrix} {M,} & {k = K_{0}} & \quad \\ {0,} & {{k = 0},1,\ldots \quad,{2K_{0}},} & {k \neq K_{0}} \end{matrix} \right.$

provided that 2K₀+1=N.
 17. The method as claimed in claim 15 wherein the a_(k)'s are calculated as a={circumflex over (B)} ⁻¹ ĉ, a being defined as $a = {\left\lbrack \begin{matrix} a_{u} & {{\left. a_{fix} \right\rbrack }^{T},} \end{matrix} \right.}$

 wherein a_(u) and a_(fix) contain (2K₀+1) unknown a_(k)'s and L=N−2K₀−1 fixed constant a_(k)'s, {circumflex over (B)}⁻¹ being the inverse of {circumflex over (B)} as given by ${\hat{B} = \begin{bmatrix} B \\ S \end{bmatrix}},$

wherein B is given by ${B = \begin{bmatrix} u_{0}^{- K_{0}} & u_{1}^{- K_{0}} & \cdots & u_{N - 1}^{- K_{0}} \\ u_{0}^{- {({K_{0} - 1})}} & u_{1}^{- {({K_{0} - 1})}} & \cdots & u_{N - 1}^{- {({K_{0} - 1})}} \\ \vdots & \vdots & \quad & \vdots \\ u_{0}^{K_{0}} & u_{1}^{K_{0}} & \cdots & u_{N - 1}^{K_{0}} \end{bmatrix}},$

 wherein $u_{k} = ^{{- j}\frac{2}{MT}t_{k}}$

S is given by S=[S _(z) S _(d)],  wherein ${S_{z} = \begin{bmatrix} 0 & 0 & \cdots & 0 \\ 0 & 0 & \cdots & 0 \\ \vdots & \vdots & \quad & \vdots \\ 0 & 0 & \cdots & 0 \end{bmatrix}},$

 and S _(d)=diag[1 1 . . . 1],  and ĉ being ĉ=[c a _(fix)]^(T),  wherein c is given by c=[c ₀ c ₁ . . . c _(2K) ₀ ]^(T),  wherein $c_{k} = \left\{ {\begin{matrix} {M,} & {k = K_{0}} & \quad \\ {0,} & {{k = 0},1,\ldots \quad,{2K_{0}},} & {k \neq K_{0}} \end{matrix},} \right.$

provided that 2K₀+1<N.
 18. The method as claimed in claim 17 wherein L=N−2K₀−1 of the a_(k)'s are calculated a_(k)=M/N, k=N−L+1, N−L+2, . . . , N.
 19. The method as claimed in claim 13 wherein N>M.
 20. The method as claimed in claim 1 wherein said N subsequences x_(k)(m) are quantized prior to being upsampled.
 21. A digital signal processing apparatus for reconstruction of a nonuniformly sampled bandlimited analog signal x_(a)(t), said nonuniformly sampled signal comprising N subsequences x_(k)(m), k=0, 1, . . . , N−1, N≧2, obtained through sampling at a sampling rate of 1/(MT) according to x_(k)(m)=x_(a)(nMT+t_(k)), where M is a positive integer, and t_(k)=kMT/N+Δt_(k), Δt_(k) being different from zero, wherein said apparatus is adapted to perform the method as claimed in claim
 1. 22. A digital signal processing apparatus for reconstruction of a nonuniformly sampled bandlimited analog signal x_(a)(t), said nonuniformly sampled signal comprising N subsequences x_(k)(m), k=0, 1, . . . , N−1, N≧2, obtained through sampling at a sampling rate of 1/(MT) according to x_(k)(m)=x_(a)(nMT+t_(k)), where M is a positive integer, and t_(k)=kMT/N+Δt_(k), Δt_(k) being different from zero, said apparatus comprising digital signal processing means for forming a new sequence y(n) from said N subsequences x_(k)(m) such that y(n) at least contains the same information as x(n)=x_(a)(nT), i.e. x_(a)(t) sampled with a sampling rate of 1/T, in a frequency region lower than ω₀, ω₀ being a predetermined limit frequency, by means of: (i) upsampling each of said N subsequences x_(k)(m), k=0, 1, . . . , N−1, by a factor M, M being a positive integer; (ii) filtering each of said upsampled N subsequences x_(k)(m), k=0, 1, . . . , N−1, by a respective digital filter; and (iii) adding said N digitally filtered subsequences to form y(n).
 23. The apparatus as claimed in claim 22 wherein the respective digital filter is a fractional delay filter and has a frequency response G_(k)=a_(k)e^((−jωsT)), k=0, 1, . . . , N−1, at least in the frequency band |ωT|≦ω₀T, a_(k) being a constant and s being different from an integer and s=d+t_(k), d being an integer.
 24. The apparatus as claimed in claim 23 wherein the a_(k)'s are calculated as a=B ⁻¹ c, a being the a_(k)'s in vector form given by a=[a ₀ a ₁ . . . a _(N−1)]^(T), B⁻¹ being the inverse of B as given by ${B = \begin{bmatrix} u_{0}^{- K_{0}} & u_{1}^{- K_{0}} & \cdots & u_{N - 1}^{- K_{0}} \\ u_{0}^{- {({K_{0} - 1})}} & u_{1}^{- {({K_{0} - 1})}} & \cdots & u_{N - 1}^{- {({K_{0} - 1})}} \\ \vdots & \vdots & \quad & \vdots \\ u_{0}^{K_{0}} & u_{1}^{K_{0}} & \cdots & u_{N - 1}^{K_{0}} \end{bmatrix}},$

 wherein ${u_{k} = ^{{- j}\frac{2\pi}{MT}t_{k}}},$

and c being c=[c ₀ c ₁ . . . c _(2K) ₀ ]^(T),  wherein $c_{k} = \left\{ {\begin{matrix} {M,} & {k = K_{0}} & \quad \\ {0,} & {{k = 0},1,\ldots \quad,{2K_{0}},} & {k \neq K_{0}} \end{matrix},} \right.$

provided that 2K₀+1=N.
 25. The apparatus as claimed in claim 23 wherein the a_(k)'s are calculated as a={circumflex over (B)} ⁻¹ ĉ, a being defined as a=[a _(u) a _(fix)]^(T),  wherein a_(u) and a_(fix) contain (2K₀+1) unknown a_(k)'s and L=N−2K₀−1 fixed constant a_(k)'s, {circumflex over (B)}⁻¹ being the inverse of {circumflex over (B)} as given by ${\hat{B} = \begin{bmatrix} B \\ S \end{bmatrix}},$

wherein B is given by ${B = \begin{bmatrix} u_{0}^{- K_{0}} & u_{1}^{- K_{0}} & \cdots & u_{N - 1}^{- K_{0}} \\ u_{0}^{- {({K_{0} - 1})}} & u_{1}^{- {({K_{0} - 1})}} & \cdots & u_{N - 1}^{- {({K_{0} - 1})}} \\ \vdots & \vdots & \quad & \vdots \\ u_{0}^{K_{0}} & u_{1}^{K_{0}} & \cdots & u_{N - 1}^{K_{0}} \end{bmatrix}},$

 wherein ${u_{k} = ^{{- j}\frac{2\pi}{MT}t_{k}}},$

S is given by S=[S _(z) S _(d)],  wherein ${S_{z} = \begin{bmatrix} 0 & 0 & \cdots & 0 \\ 0 & 0 & \cdots & 0 \\ \vdots & \vdots & \quad & \vdots \\ 0 & 0 & \cdots & 0 \end{bmatrix}},$

 and S _(d)=diag[1 1 . . . 1],  and ĉ being ĉ=[c a _(fix)]^(T),  wherein c is given by c=[c ₀ c ₁ . . . c _(2K) ₀ ]^(T),  wherein $c_{k} = \left\{ {\begin{matrix} {M,} & {k = K_{0}} & \quad \\ {0,} & {{k = 0},1,\ldots \quad,{2K_{0}},} & {k \neq K_{0}} \end{matrix},} \right.$

provided that 2K₀+1<N.
 26. A method for compensation of time skew in a time-interleaved analog-to-digital converter (ADC) system comprising a plurality of analog-to-digital converters (ADCs), said method comprising performing the method as claimed in claim 1 wherein each of said N subsequences x_(k)(m), k=0, 1, . . . , N−1, N≧2 is sampled by a respective one of said analog-to-digital converters.
 27. A time-interleaved analog-to-digital converter (ADC) system comprising a plurality of analog-to-digital converters (ADCs), said system comprising a digital signal processing apparatus as claimed in claim 21, wherein each of said N subsequences x_(k)(m), k=0, 1, . . . , N−1, N≧2 is sampled by a respective one of said analog-to-digital converters.
 28. A computer program product loadable into the internal memory of a digital signal processing apparatus, comprising software code portions for performing the method as claimed in claim 1 when said product is run on said apparatus. 